Assessing the Reliability of the MODIS LST Product to Detect Temporal Variability

Abstract

Land surface temperature (LST) data acquired from satellites are used extensively in studying climate variability. Many researchers have used moderate resolution imaging spectroradiometer (MODIS) LST to detect the temperature trend, however, its reliability has not been fully investigated. Using in situ data acquired from 67 stations worldwide, this study examined the reliability of the detected temperature trends and investigated the associated influencing factors. The high-quality (HQ) MODIS data have a root mean square error (RMSE) of 2.44 and 3.70 K at nighttime and daytime, respectively. However, its trend detection had an RMSE of 0.81 and 0.98 K/decade at nighttime and daytime, respectively. Clear-sky bias, quality control, LST estimation uncertainties, trend magnitude, and length of time were factors that influenced the detected trends. Filling cloud-covered areas in MODIS data may effectively reduce biases in trend detection.